Overview

Dataset statistics

Number of variables13
Number of observations786600
Missing cells24767
Missing cells (%)0.2%
Duplicate rows546
Duplicate rows (%)0.1%
Total size in memory78.0 MiB
Average record size in memory104.0 B

Variable types

Categorical4
Numeric9

Warnings

Dataset has 546 (0.1%) duplicate rows Duplicates
customer_id has a high cardinality: 245455 distinct values High cardinality
order_date has a high cardinality: 776 distinct values High cardinality
customer_order_rank has 24767 (3.1%) missing values Missing
voucher_amount is highly skewed (γ1 = 30.39394065) Skewed
platform_id is highly skewed (γ1 = -22.53663783) Skewed
voucher_amount has 743462 (94.5%) zeros Zeros
delivery_fee has 597536 (76.0%) zeros Zeros

Reproduction

Analysis started2021-02-25 23:03:01.794157
Analysis finished2021-02-25 23:03:58.065411
Duration56.27 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

customer_id
Categorical

HIGH CARDINALITY

Distinct245455
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
15edce943edd
 
386
8745a335e9cf
 
288
d956116d863d
 
286
0063666607bb
 
273
ae60dce05485
 
270
Other values (245450)
785097 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters9439200
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145498 ?
Unique (%)18.5%

Sample

1st row000097eabfd9
2nd row0000e2c6d9be
3rd row000133bb597f
4th row00018269939b
5th row0001a00468a6
ValueCountFrequency (%)
15edce943edd386
 
< 0.1%
8745a335e9cf288
 
< 0.1%
d956116d863d286
 
< 0.1%
0063666607bb273
 
< 0.1%
ae60dce05485270
 
< 0.1%
a54a8e1579d4254
 
< 0.1%
bebb751d49b8253
 
< 0.1%
26ed6389a3aa245
 
< 0.1%
ef6265f74aca229
 
< 0.1%
a333fb175a0c221
 
< 0.1%
Other values (245445)783895
99.7%
2021-02-25T17:03:58.943165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15edce943edd386
 
< 0.1%
8745a335e9cf288
 
< 0.1%
d956116d863d286
 
< 0.1%
0063666607bb273
 
< 0.1%
ae60dce05485270
 
< 0.1%
a54a8e1579d4254
 
< 0.1%
bebb751d49b8253
 
< 0.1%
26ed6389a3aa245
 
< 0.1%
ef6265f74aca229
 
< 0.1%
a333fb175a0c221
 
< 0.1%
Other values (245445)783895
99.7%

Most occurring characters

ValueCountFrequency (%)
6594904
 
6.3%
4594873
 
6.3%
0594664
 
6.3%
8591857
 
6.3%
b591838
 
6.3%
d590973
 
6.3%
5590840
 
6.3%
e589501
 
6.2%
2589442
 
6.2%
3589119
 
6.2%
Other values (6)3521189
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5907912
62.6%
Lowercase Letter3531288
37.4%

Most frequent character per category

ValueCountFrequency (%)
6594904
10.1%
4594873
10.1%
0594664
10.1%
8591857
10.0%
5590840
10.0%
2589442
10.0%
3589119
10.0%
7587458
9.9%
9587405
9.9%
1587350
9.9%
ValueCountFrequency (%)
b591838
16.8%
d590973
16.7%
e589501
16.7%
f589006
16.7%
a586882
16.6%
c583088
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common5907912
62.6%
Latin3531288
37.4%

Most frequent character per script

ValueCountFrequency (%)
6594904
10.1%
4594873
10.1%
0594664
10.1%
8591857
10.0%
5590840
10.0%
2589442
10.0%
3589119
10.0%
7587458
9.9%
9587405
9.9%
1587350
9.9%
ValueCountFrequency (%)
b591838
16.8%
d590973
16.7%
e589501
16.7%
f589006
16.7%
a586882
16.6%
c583088
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII9439200
100.0%

Most frequent character per block

ValueCountFrequency (%)
6594904
 
6.3%
4594873
 
6.3%
0594664
 
6.3%
8591857
 
6.3%
b591838
 
6.3%
d590973
 
6.3%
5590840
 
6.3%
e589501
 
6.2%
2589442
 
6.2%
3589119
 
6.2%
Other values (6)3521189
37.3%

order_date
Categorical

HIGH CARDINALITY

Distinct776
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2017-01-01
 
4230
2016-12-18
 
3395
2017-02-26
 
3234
2017-02-05
 
3218
2017-02-12
 
3125
Other values (771)
769398 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7866000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)< 0.1%

Sample

1st row2015-06-20
2nd row2016-01-29
3rd row2017-02-26
4th row2017-02-05
5th row2015-08-04
ValueCountFrequency (%)
2017-01-014230
 
0.5%
2016-12-183395
 
0.4%
2017-02-263234
 
0.4%
2017-02-053218
 
0.4%
2017-02-123125
 
0.4%
2016-12-113100
 
0.4%
2016-12-043075
 
0.4%
2017-01-223005
 
0.4%
2017-01-293003
 
0.4%
2016-10-032999
 
0.4%
Other values (766)754216
95.9%
2021-02-25T17:03:59.207108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-01-014230
 
0.5%
2016-12-183395
 
0.4%
2017-02-263234
 
0.4%
2017-02-053218
 
0.4%
2017-02-123125
 
0.4%
2016-12-113100
 
0.4%
2016-12-043075
 
0.4%
2017-01-223005
 
0.4%
2017-01-293003
 
0.4%
2016-10-032999
 
0.4%
Other values (766)754216
95.9%

Most occurring characters

ValueCountFrequency (%)
01727194
22.0%
-1573200
20.0%
11537463
19.5%
21281258
16.3%
6599766
 
7.6%
5343454
 
4.4%
7243724
 
3.1%
3166297
 
2.1%
8135891
 
1.7%
9135707
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6292800
80.0%
Dash Punctuation1573200
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
01727194
27.4%
11537463
24.4%
21281258
20.4%
6599766
 
9.5%
5343454
 
5.5%
7243724
 
3.9%
3166297
 
2.6%
8135891
 
2.2%
9135707
 
2.2%
4122046
 
1.9%
ValueCountFrequency (%)
-1573200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7866000
100.0%

Most frequent character per script

ValueCountFrequency (%)
01727194
22.0%
-1573200
20.0%
11537463
19.5%
21281258
16.3%
6599766
 
7.6%
5343454
 
4.4%
7243724
 
3.1%
3166297
 
2.1%
8135891
 
1.7%
9135707
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII7866000
100.0%

Most frequent character per block

ValueCountFrequency (%)
01727194
22.0%
-1573200
20.0%
11537463
19.5%
21281258
16.3%
6599766
 
7.6%
5343454
 
4.4%
7243724
 
3.1%
3166297
 
2.1%
8135891
 
1.7%
9135707
 
1.7%

order_hour
Real number (ℝ≥0)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.58879608
Minimum0
Maximum23
Zeros4627
Zeros (%)0.6%
Memory size6.0 MiB
2021-02-25T17:03:59.306247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q116
median18
Q320
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.357192477
Coefficient of variation (CV)0.1908710785
Kurtosis5.749711941
Mean17.58879608
Median Absolute Deviation (MAD)2
Skewness-1.749088644
Sum13835347
Variance11.27074133
MonotocityNot monotonic
2021-02-25T17:03:59.407573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
19134030
17.0%
18129654
16.5%
20108739
13.8%
1790782
11.5%
2168223
8.7%
1648877
 
6.2%
1534286
 
4.4%
2233403
 
4.2%
1331105
 
4.0%
1430323
 
3.9%
Other values (14)77178
9.8%
ValueCountFrequency (%)
04627
0.6%
12425
0.3%
21187
 
0.2%
3443
 
0.1%
4137
 
< 0.1%
ValueCountFrequency (%)
2313832
 
1.8%
2233403
 
4.2%
2168223
8.7%
20108739
13.8%
19134030
17.0%

customer_order_rank
Real number (ℝ≥0)

MISSING

Distinct369
Distinct (%)< 0.1%
Missing24767
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean9.436809642
Minimum1
Maximum369
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-02-25T17:03:59.521888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310
95-th percentile39
Maximum369
Range368
Interquartile range (IQR)9

Descriptive statistics

Standard deviation17.77232218
Coefficient of variation (CV)1.88329773
Kurtosis49.04720204
Mean9.436809642
Median Absolute Deviation (MAD)2
Skewness5.494014541
Sum7189273
Variance315.8554356
MonotocityNot monotonic
2021-02-25T17:03:59.641275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1244937
31.1%
296641
 
12.3%
360532
 
7.7%
443681
 
5.6%
534036
 
4.3%
627603
 
3.5%
723049
 
2.9%
819696
 
2.5%
917013
 
2.2%
1014889
 
1.9%
Other values (359)179756
22.9%
(Missing)24767
 
3.1%
ValueCountFrequency (%)
1244937
31.1%
296641
 
12.3%
360532
 
7.7%
443681
 
5.6%
534036
 
4.3%
ValueCountFrequency (%)
3691
< 0.1%
3681
< 0.1%
3671
< 0.1%
3661
< 0.1%
3651
< 0.1%

is_failed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
0
761833 
1
 
24767

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters786600
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0761833
96.9%
124767
 
3.1%
2021-02-25T17:03:59.884044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T17:03:59.977899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0761833
96.9%
124767
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0761833
96.9%
124767
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number786600
100.0%

Most frequent character per category

ValueCountFrequency (%)
0761833
96.9%
124767
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common786600
100.0%

Most frequent character per script

ValueCountFrequency (%)
0761833
96.9%
124767
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII786600
100.0%

Most frequent character per block

ValueCountFrequency (%)
0761833
96.9%
124767
 
3.1%

voucher_amount
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct911
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09148909292
Minimum0
Maximum93.3989
Zeros743462
Zeros (%)94.5%
Memory size6.0 MiB
2021-02-25T17:04:00.077602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.686
Maximum93.3989
Range93.3989
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4795579176
Coefficient of variation (CV)5.241694963
Kurtosis3886.352852
Mean0.09148909292
Median Absolute Deviation (MAD)0
Skewness30.39394065
Sum71965.32049
Variance0.2299757963
MonotocityNot monotonic
2021-02-25T17:04:00.217853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0743462
94.5%
1.02911647
 
1.5%
1.71511134
 
1.4%
2.0589122
 
1.2%
0.6863648
 
0.5%
1.3721770
 
0.2%
2.7441192
 
0.2%
2.5725897
 
0.1%
3.43543
 
0.1%
0.5145373
 
< 0.1%
Other values (901)2812
 
0.4%
ValueCountFrequency (%)
0743462
94.5%
0.0034335
 
< 0.1%
0.284691
 
< 0.1%
0.322421
 
< 0.1%
0.34319
 
< 0.1%
ValueCountFrequency (%)
93.39891
< 0.1%
78.029071
< 0.1%
68.39421
< 0.1%
61.825751
< 0.1%
37.575651
< 0.1%

delivery_fee
Real number (ℝ≥0)

ZEROS

Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1811799318
Minimum0
Maximum9.86
Zeros597536
Zeros (%)76.0%
Memory size6.0 MiB
2021-02-25T17:04:00.355002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.986
Maximum9.86
Range9.86
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3697095668
Coefficient of variation (CV)2.040565769
Kurtosis8.481347092
Mean0.1811799318
Median Absolute Deviation (MAD)0
Skewness2.417459196
Sum142516.1343
Variance0.1366851638
MonotocityNot monotonic
2021-02-25T17:04:00.497526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0597536
76.0%
0.49370617
 
9.0%
0.98635735
 
4.5%
0.739534790
 
4.4%
0.24657664
 
1.0%
1.23257164
 
0.9%
1.4796768
 
0.9%
1.42975078
 
0.6%
0.468353097
 
0.4%
0.44372657
 
0.3%
Other values (88)15494
 
2.0%
ValueCountFrequency (%)
0597536
76.0%
0.0246510
 
< 0.1%
0.04933
 
< 0.1%
0.09864
 
< 0.1%
0.1479303
 
< 0.1%
ValueCountFrequency (%)
9.861
< 0.1%
7.3951
< 0.1%
6.65551
< 0.1%
6.4091
< 0.1%
5.9161
< 0.1%

amount_paid
Real number (ℝ≥0)

Distinct6471
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.18327131
Minimum0
Maximum1131.03
Zeros872
Zeros (%)0.1%
Memory size6.0 MiB
2021-02-25T17:04:00.637447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.5135
Q16.64812
median9.027
Q312.213
95-th percentile19.5408
Maximum1131.03
Range1131.03
Interquartile range (IQR)5.56488

Descriptive statistics

Standard deviation5.6181212
Coefficient of variation (CV)0.5517010233
Kurtosis2243.912588
Mean10.18327131
Median Absolute Deviation (MAD)2.655
Skewness15.5881411
Sum8010161.21
Variance31.56328582
MonotocityNot monotonic
2021-02-25T17:04:00.763460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3114667
 
1.9%
7.96514410
 
1.8%
6.37211878
 
1.5%
8.49610350
 
1.3%
6.9039988
 
1.3%
5.8419734
 
1.2%
9.0279213
 
1.2%
7.4349156
 
1.2%
10.628982
 
1.1%
9.5588377
 
1.1%
Other values (6461)679845
86.4%
ValueCountFrequency (%)
0872
0.1%
0.005311
 
< 0.1%
0.015931
 
< 0.1%
0.026551
 
< 0.1%
0.037171
 
< 0.1%
ValueCountFrequency (%)
1131.031
< 0.1%
581.71051
< 0.1%
363.018151
< 0.1%
353.38051
< 0.1%
246.888451
< 0.1%

restaurant_id
Real number (ℝ≥0)

Distinct13569
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162864079.3
Minimum73498
Maximum340453498
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-02-25T17:04:00.914589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum73498
5-th percentile29803498
Q186023498
median169613498
Q3228433498
95-th percentile302393498
Maximum340453498
Range340380000
Interquartile range (IQR)142410000

Descriptive statistics

Standard deviation87830821.23
Coefficient of variation (CV)0.5392890906
Kurtosis-1.08595334
Mean162864079.3
Median Absolute Deviation (MAD)71240000
Skewness-0.02254910338
Sum1.281088848 × 1014
Variance7.714253157 × 1015
MonotocityNot monotonic
2021-02-25T17:04:01.074691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
376234981317
 
0.2%
9834981071
 
0.1%
1926734981031
 
0.1%
154543498999
 
0.1%
88773498967
 
0.1%
146723498942
 
0.1%
105253498935
 
0.1%
18603498922
 
0.1%
30633498918
 
0.1%
29593498882
 
0.1%
Other values (13559)776616
98.7%
ValueCountFrequency (%)
73498120
< 0.1%
12349837
 
< 0.1%
153498193
< 0.1%
173498181
< 0.1%
19349884
< 0.1%
ValueCountFrequency (%)
3404534981
< 0.1%
3400934982
< 0.1%
3400334981
< 0.1%
3399834982
< 0.1%
3399134981
< 0.1%

city_id
Real number (ℝ≥0)

Distinct3749
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47179.7505
Minimum230
Maximum100205
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-02-25T17:04:01.222492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum230
5-th percentile10346
Q124799
median46467
Q367886
95-th percentile89749
Maximum100205
Range99975
Interquartile range (IQR)43087

Descriptive statistics

Standard deviation25904.63056
Coefficient of variation (CV)0.5490624747
Kurtosis-1.018564164
Mean47179.7505
Median Absolute Deviation (MAD)21419
Skewness0.05185593619
Sum3.711159174 × 1010
Variance671049884.7
MonotocityNot monotonic
2021-02-25T17:04:01.362397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1034686654
 
11.0%
2032636210
 
4.6%
8056234100
 
4.3%
5089821627
 
2.7%
4044116732
 
2.1%
6053714760
 
1.9%
4436614119
 
1.8%
4535811246
 
1.4%
433411106
 
1.4%
9063310449
 
1.3%
Other values (3739)529597
67.3%
ValueCountFrequency (%)
230993
 
0.1%
12986519
0.8%
167677
 
< 0.1%
168533
 
< 0.1%
168918
 
< 0.1%
ValueCountFrequency (%)
1002051
 
< 0.1%
1000791
 
< 0.1%
1000613
 
< 0.1%
10004856
< 0.1%
999995
 
< 0.1%

payment_id
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
1619
476600 
1779
234133 
1491
 
36497
1811
 
34492
1523
 
4878

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3146400
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1779
2nd row1619
3rd row1619
4th row1619
5th row1619
ValueCountFrequency (%)
1619476600
60.6%
1779234133
29.8%
149136497
 
4.6%
181134492
 
4.4%
15234878
 
0.6%
2021-02-25T17:04:01.781584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T17:04:01.858901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1619476600
60.6%
1779234133
29.8%
149136497
 
4.6%
181134492
 
4.4%
15234878
 
0.6%

Most occurring characters

ValueCountFrequency (%)
11368681
43.5%
9747230
23.7%
6476600
 
15.1%
7468266
 
14.9%
436497
 
1.2%
834492
 
1.1%
54878
 
0.2%
24878
 
0.2%
34878
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3146400
100.0%

Most frequent character per category

ValueCountFrequency (%)
11368681
43.5%
9747230
23.7%
6476600
 
15.1%
7468266
 
14.9%
436497
 
1.2%
834492
 
1.1%
54878
 
0.2%
24878
 
0.2%
34878
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common3146400
100.0%

Most frequent character per script

ValueCountFrequency (%)
11368681
43.5%
9747230
23.7%
6476600
 
15.1%
7468266
 
14.9%
436497
 
1.2%
834492
 
1.1%
54878
 
0.2%
24878
 
0.2%
34878
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3146400
100.0%

Most frequent character per block

ValueCountFrequency (%)
11368681
43.5%
9747230
23.7%
6476600
 
15.1%
7468266
 
14.9%
436497
 
1.2%
834492
 
1.1%
54878
 
0.2%
24878
 
0.2%
34878
 
0.2%

platform_id
Real number (ℝ≥0)

SKEWED

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29868.52938
Minimum525
Maximum30423
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-02-25T17:04:01.949479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum525
5-th percentile29463
Q129463
median29815
Q330231
95-th percentile30359
Maximum30423
Range29898
Interquartile range (IQR)768

Descriptive statistics

Standard deviation1160.893265
Coefficient of variation (CV)0.03886677012
Kurtosis565.3036862
Mean29868.52938
Median Absolute Deviation (MAD)352
Skewness-22.53663783
Sum2.349458521 × 1010
Variance1347673.174
MonotocityNot monotonic
2021-02-25T17:04:02.042417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
29463241523
30.7%
30231216726
27.6%
29815158972
20.2%
30359103653
13.2%
3039124434
 
3.1%
2975119321
 
2.5%
2949511151
 
1.4%
304236819
 
0.9%
301992079
 
0.3%
5251094
 
0.1%
Other values (4)828
 
0.1%
ValueCountFrequency (%)
5251094
 
0.1%
221673
 
< 0.1%
22263232
 
< 0.1%
222951
 
< 0.1%
29463241523
30.7%
ValueCountFrequency (%)
304236819
 
0.9%
3039124434
 
3.1%
30359103653
13.2%
30231216726
27.6%
301992079
 
0.3%

transmission_id
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4253.246112
Minimum212
Maximum21124
Zeros0
Zeros (%)0.0%
Memory size6.0 MiB
2021-02-25T17:04:02.150246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum212
5-th percentile4228
Q14228
median4324
Q34356
95-th percentile4356
Maximum21124
Range20912
Interquartile range (IQR)128

Descriptive statistics

Standard deviation572.8556657
Coefficient of variation (CV)0.1346866959
Kurtosis176.6261099
Mean4253.246112
Median Absolute Deviation (MAD)32
Skewness-0.9114324558
Sum3345603392
Variance328163.6137
MonotocityNot monotonic
2021-02-25T17:04:02.250487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4356341734
43.4%
4324203668
25.9%
4228201617
25.6%
426014538
 
1.8%
21212676
 
1.6%
49966737
 
0.9%
41965276
 
0.7%
1988207
 
< 0.1%
21124146
 
< 0.1%
20201
 
< 0.1%
ValueCountFrequency (%)
21212676
 
1.6%
1988207
 
< 0.1%
20201
 
< 0.1%
41965276
 
0.7%
4228201617
25.6%
ValueCountFrequency (%)
21124146
 
< 0.1%
49966737
 
0.9%
4356341734
43.4%
4324203668
25.9%
426014538
 
1.8%

Interactions

2021-02-25T17:03:35.934842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:36.215729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:36.460612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:36.692167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:36.937200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:37.175397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:37.408342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:37.646625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:37.902229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:38.157215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:38.431998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:38.694258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:38.948234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:39.202636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:39.441075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:39.687186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:39.949355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:40.177501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:40.425154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:40.645222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:40.884737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:41.126464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:41.351127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:41.584812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:41.837318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:42.088082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:42.348564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:42.569532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:42.820763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:43.067403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:43.294561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:43.530685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:43.772380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:43.993877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:44.246611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:44.504645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:44.759511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:45.003280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:45.236764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:45.478238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:45.728148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:45.974804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:46.227206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:46.491471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:46.751694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:47.027027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:47.288688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:47.551039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:47.813542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:48.047224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:48.294378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:48.526593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:48.759483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:49.001758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:49.246614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:49.480671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:49.722218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:49.983886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:50.263120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:50.542493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:50.864319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:51.157420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:51.434437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:51.679436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:51.920092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:52.148843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:52.389693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:52.626076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:53.044930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:53.279016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:53.511292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T17:03:53.748651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-25T17:04:02.367944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-25T17:04:02.592075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-25T17:04:02.802942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-25T17:04:03.021498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-25T17:04:03.188167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-25T17:03:54.471734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-25T17:03:55.462378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-25T17:03:57.536942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

customer_idorder_dateorder_hourcustomer_order_rankis_failedvoucher_amountdelivery_feeamount_paidrestaurant_idcity_idpayment_idplatform_idtransmission_id
0000097eabfd92015-06-20191.000.00.00011.469605803498203261779302314356
10000e2c6d9be2016-01-29201.000.00.0009.55800239303498765471619303594356
2000133bb597f2017-02-26191.000.00.4935.93658206463498338331619303594324
300018269939b2017-02-05171.000.00.4939.8235036613498993151619303594356
40001a00468a62015-08-04191.000.00.4935.15070225853498164561619294634356
50001d9036b5e2015-08-29191.000.00.00011.94750193643498882761619294634356
60001d9036b5e2017-01-04172.000.00.00011.15100193643498882761619294634356
70001d9036b5e2017-01-28163.000.00.0009.71730193643498882761619303594356
80001e1e04d7d2015-10-24191.000.00.00025.22250144833498453581619294634356
90001e1e04d7d2016-03-24192.000.00.0009.2925095953498453581619294634324

Last rows

customer_idorder_dateorder_hourcustomer_order_rankis_failedvoucher_amountdelivery_feeamount_paidrestaurant_idcity_idpayment_idplatform_idtransmission_id
786590fffcf45e5c692016-11-19121.000.00.000012.53160107463498393351619294634356
786591fffcf45e5c692017-02-04122.000.00.000011.57580107463498393351619303594356
786592fffd696eaedd2015-09-14121.000.01.429724.1339595323498805621779294634356
786593fffe9d5a8d412016-07-3121NaN10.00.00008.4429015613349810346181129463212
786594fffe9d5a8d412016-09-30201.000.00.000010.72620983498103461779294634228
786595fffe9d5a8d412016-09-3020NaN10.00.000010.7262098349810346177929463212
786596ffff347c3cfa2016-08-17211.000.00.00007.5933052893498419781619303594356
786597ffff347c3cfa2016-09-15212.000.00.00005.94720164653498419781619303594356
786598ffff4519b52d2016-04-02191.000.00.000021.7710016363498805621491297514228
786599ffffccbfc8a42015-05-30201.000.00.000016.46100150293498459521619294634324